1. Field of the Invention
The present invention relates to interactive research tools. More specifically, the invention relates to a visualization-based interactive research tool that allows researchers to study individual legal issues of interest.
2. Related Art
The U.S. and some other countries follow a common law system, in which laws developed over centuries and were largely derived from judicial opinions. The legal systems in these countries are based on the doctrines implicit in court decisions, customs, and usages, rather than on codified written rules. Common laws rely heavily on the concept of precedence—on how the courts have interpreted the law in individual cases (hence, the term case law). This reliance by the legal system on precedent makes it critical for legal practitioners to study case citations—how issues related to his or her current case were discussed and ruled on in previous cases.
When an attorney starts research with a legal problem in mind, he or she goes through a repetitive mental process of forward and backward searching in the imaginary space of legal issues embodied mainly by previous cases. This kind of mental model, by way of which the attorney's cognitive map of a legal doctrine in question is built, is discussed by Sutton (Stuart A. Sutton, “The Role of Attorney Mental Models of Law in Case Relevance Determinations: An Exploratory Analysis,” Journal of the American Society for Information Science, 45(3): 186-200) (1994)). In this type of research, as described by Sutton, the attorney employs one or more seed cases to engage in a practice that is variously referred to as “gathering citations” (S. K. Stoan, “Research and Library Skills: An Analysis and Interpretation,” College & Research Libraries, 45:99-109 (1984)), “chaining” (D. Ellis, “A Behavioral Approach to Information Retrieval System Design,” Journal of Documentation, 45: 171-212 (1989)), and “footnote chasing” and “citation searching” (M. J. Bates, “Where Should the Person Stop and the Information Search Interface Start?,” Information Processing & Management, 26:575-591 (1989)). FIG. 5 (which is taken from Sutton) depicts part of this process as a general attorney behavior model. In the center of FIG. 5 is the seed or root case of interest 50. The arrows represent the direction of the searcher's chaining, and the passage of time is represented by the position of each case, i.e. from left to right. From the Known Seed Case 15 (in the center), the attorney first finds Case 18 and Case 19 through Shepardizing (a term that means finding cases that cited a given case in the legal corpus). He then finds Cases 11 and 12 by Internal Tracking, which involves reading the document and searching for more citations. Here the search is bi-directional: forward chaining to find cases that cited the current case, and backward chaining to find cases that the current case cited to. The whole process is recursive; at each step the researcher finds one or more cases. Each of these new cases is then used to trace and find more cases in the same manner. Marx (Stephen M. Marx, “Citation Networks in the Law,” Jurimetrics Journal, 1970:121-137) called this mental process “exhaustive Shepardizing,” and noted that, since cases are cited for numerous legal propositions, many of which may not be relevant to the current problem, this mental process is really a “selective process.”
There are tools and services that aim to assist attorneys in this kind of research. Citator services (e.g., LexisNexis's Shepard's®, and WestLaw's KeyCite®) allow the user to see the whole list of citations that directly reference to a given case. The legal information retrieval (IR) and artificial intelligence (AI) fields have also been offering help, as discussed by K. Ashley et al., “An Introduction to Artificial Intelligence and Law,” Tutorial Handout of Introduction to AI and Law at ICAIL (2005). Search-based tools can identify cases that are conceptually close to what the user needs by searching with key words the user enters, or by matching important terms between two cases. AI-based techniques, such as machine learning, are also used for relevant prior case retrieval, as described by Al-Kofahi et al., A Machine Learning Approach to Prior Case Retrieval, ICAIL-2000). All these tools and services help the researchers tremendously in each of the steps described above. More recently, use of legal taxonomy, ontology, or semantic networks has been brought to the legal IR field (see Hooge et al., “Semantics in the Legal Domain,” from Web at: www.arches.uga.edu/˜jhassell/project/legal_paper.pdf (2004); Ashley et al.; Schild et al., “A Taxonomy for Modeling Discretionary Decision Making in the Legal Domain,” Proceedings of the Tenth International Conference on Artificial Intelligence and Law (2005); Winkels et al., “Constructing a Semantic Network for Legal Content,” Proceedings of the Tenth International Conference on Artificial Intelligence and Law (2005); Lame et al., “Updating Ontologies in the Legal Domain,” Proceedings of the Tenth International Conference on Artificial Intelligence and Law (2005); Bourcier et al., “Methodological Perspectives for Legal Ontologies Building: an Interdisciplinary Experience,” Proceedings of the Tenth International Conference on Artificial Intelligence and Law (2005); Walter et al., “Computational Linguistic Support for Legal Ontology Construction,” Proceedings of the Tenth International Conference on Artificial Intelligence and Law (2005). However, to do a decent job, the attorney, at each step of his research, has to sift through many case documents before he can move to the next search stage. This exhaustive and selective search process required by traditional methods is very time-consuming, and the results depend, to a large extent, on the issue in question and the accuracy of the search tools used.
When an opinion for a case is written, the author often cites previous cases in support of his or her own reasoning; these cases, in turn, have cited others cases for the same purpose. Over time, these citing-cited relations between cases form a network, referred to herein as “the general citation network.” The citation relations in the network are complicated; but they are non-arbitrary as “citational links exist because at some point in time a judge and a lawyer decided that a logical connection existed between certain cases” (Marx). It follows that knowledge embedded in a citation network can be a valuable source for attorneys and legal scholars.
Legal professionals and computer scientists have been interested in this phenomenon. Smith, (“The Web of Law,” San Diego Legal Studies Research Paper No. 06-11, http://ssrn.com/abstract=642863 (2005)), after a thorough study of the American case citations, concluded that the law system “suggests a high degree of intellectual coherence”, and that “studying the legal network can shed light on how the legal system evolves and many other questions.” BankXX, a system proposed by Rissland et al. (Rissland et al., “BankXX: Supporting Legal Arguments through Heuristic Retrieval,” Artificial Intelligence and Law, 1996(4): 1-71)) to support legal argumentation, uses citation links between cases in its knowledge base. Hooge et al. describe the LLT Program, which creates a “Legal Logic Tree” for a given case based on citation relations between cases.
However, the researcher lacks a means to see clearly the relationships between all the discussions linked by citations, and a means to quickly view other issues discussed in parallel to the starting issue, because the existing, general citation network is multi-dimensional. This multi-dimensionality exists because a case can cite each of several cases for a different reason; and, likewise, a case can be cited by other cases for different reasons, represented by different line patterns in FIG. 4A. Two citations pointing to the same case may not necessarily be semantically related because they may each be based on a different legal issue. This multi-dimensionality poses a problem to legal researchers who want to focus on individual legal issues because they have to read all retrieved cases to select ones that are on issues of interest. This multi-dimensionality has also made use of existing legal citation networks impractical, as a general network traversing function would retrieve indiscriminately many cases and soon fill the screen space where the retrieved network would be displayed, making viewing and reading impossible.
It is to the solution of these and other problems that the present invention is directed.